class_docstr: Implements an ML preprocessing operation.
  
  Preprocessors are stateful objects that can be fitted against a Dataset and used
  to transform both local data batches and distributed data. For example, a
  Normalization preprocessor may calculate the mean and stdev of a field during
  fitting, and uses these attributes to implement its normalization transform.
  
  Preprocessors can also be stateless and transform data without needed to be fitted.
  For example, a preprocessor may simply remove a column, which does not require
  any state to be fitted.
  
  If you are implementing your own Preprocessor sub-class, you should override the
  following:
  
  * ``_fit`` if your preprocessor is stateful. Otherwise, set
    ``_is_fittable=False``.
  * ``_transform_pandas`` and/or ``_transform_numpy`` for best performance,
    implement both. Otherwise, the data will be converted to the match the
    implemented method.
docstr: Transform the given dataset.
  
  Args:
      ds: Input Dataset.
  
  Returns:
      ray.data.Dataset: The transformed Dataset.
  
  Raises:
      PreprocessorNotFittedException: if ``fit`` is not called yet.
docstr_tok: ["Transform","the","given","dataset",".","Args",":","ds",":","Input","Dataset",".","Returns",":","ray.data.Dataset",":","The","transformed","Dataset",".","Raises",":","PreprocessorNotFittedException",":","if","``","fit","``","is","not","called","yet","."]
code: def transform(self, ds: "Dataset") -> "Dataset":
      """Transform the given dataset.
  
      Args:
          ds: Input Dataset.
  
      Returns:
          ray.data.Dataset: The transformed Dataset.
  
      Raises:
          PreprocessorNotFittedException: if ``fit`` is not called yet.
      """
      fit_status = self.fit_status()
      if fit_status in (
          Preprocessor.FitStatus.PARTIALLY_FITTED,
          Preprocessor.FitStatus.NOT_FITTED,
      ):
          raise PreprocessorNotFittedException(
              "`fit` must be called before `transform`, "
              "or simply use fit_transform() to run both steps"
          )
      transformed_ds = self._transform(ds)
      return transformed_ds
  
imports: import abc
  import base64
  import collections
  import pickle
  import warnings
  from enum import Enum
  from typing import TYPE_CHECKING, Any, Dict, Optional, Union
  from ray.air.util.data_batch_conversion import BatchFormat
  from ray.util.annotations import Deprecated, DeveloperAPI, PublicAPI
label_desc: Use image node_id 6 for calling the Preprocessor obj's underlying member method code with example usage: obj.transform(ds) and returns: transformed_ds